tbuckley/cabot-search huggingface dataset:
Abstract 126: Defining how oxidative stress drives the evolution of aggressive breast cancers
Authors: Caitlynn N. Mirabelli, Rachel La Selva, John Heath, Steven Hébert, Jutta Steinberger, Jialin Jiang, Claudia Kleinman, Sidong Huang, Josie Ursini-Siegel
Journal: Cancer Research (2022)

Abstract Breast cancer is the most commonly diagnosed malignancy in women worldwide. It is a molecularly heterogeneous disease, which poses difficulties in the treatment of more advanced cancers. Despite this fact, the mechanisms contributing to the emergence of aggressive breast cancers remains poorly understood. We focus on reactive oxygen species (ROS), which are induced by a variety of stimuli in breast cancer cells. Moderately elevated ROS levels are tumor-promoting and facilitate the emergence of more aggressive tumours. Our laboratory has developed a unique model of HER2+ breast cancer that evolved to acquire more aggressive properties under conditions of chronic oxidative stress. We identified 20 transcription factors/co-regulators that were specifically overexpressed in these aggressive breast cancers, compared to their parental counterparts. We hypothesize that these transcription factors are central to the ability of breast tumors to adapt to chronic oxidative stress and acquire more aggressive properties. To test this, I have performed an in vivo functional shRNA screen to identify those transcriptional regulators that drive an aggressive phenotype. This will be followed by functional validation studies to interrogate whether and how these unique transcription factors overexpressed in aggressive breast cancers impact tumour growth, metastasis, and drug resistance. Mechanistically, we will explore whether and how these transcriptional responses mediate adaptive responses to tumor microenvironmental stressors, including altered metabolism, hypoxia, and tumor immune responses. This research will broaden our understanding of how breast cancers adapt to oxidative stress responses and represents a necessary first step in the development of novel therapeutics against these invulnerable malignancies. Citation Format: Caitlynn N. Mirabelli, Rachel La Selva, John Heath, Steven Hébert, Jutta Steinberger, Jialin Jiang, Claudia Kleinman, Sidong Huang, Josie Ursini-Siegel. Defining how oxidative stress drives the evolution of aggressive breast cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 126.
Tofacitinib and Risk of Malignancy: Results From the Safety of Tofacitinib in Routine Care Patients With Rheumatoid Arthritis (STAR‐RA) Study
Authors: Farzin Khosrow‐Khavar, Rishi J. Desai, Hemin Lee, Su Been Lee, Seoyoung C. Kim
Journal: Arthritis & Rheumatology (2022)

Results of the ORAL Surveillance safety trial have indicated that there is an increased risk for the development of malignancies with tofacitinib therapy when compared to treatment with tumor necrosis factor inhibitors (TNFi). This study was undertaken to further examine this safety concern in rheumatoid arthritis (RA) patients in a real-world setting.
Abstract 5170: Coding and non-coding gene meta-features predict outcome in pediatric rhabdomyosarcomas
Authors: Anirban P. Mitra, Sheetal A. Mitra, Jonathan D. Buckley, Stephen X. Skapek, Douglas S. Hawkins, Timothy J. Triche
Journal: Cancer Research (2022)

Abstract INTRODUCTION: Pediatric rhabdomyosarcoma (RMS) has varying outcomes, particularly in intermediate-risk disease (IR-RMS) due to the limited ability of clinical staging to accurately risk-stratify a large proportion of patients. This study aimed to identify prognostic signatures in IR-RMS, the clinical subgroup with the most heterogeneous outcomes, which can potentially improve risk stratification compared with routine clinicopathologic metrics. Signature performance was validated on an independent set of RMS patients. METHODS: Prospectively obtained primary tumor specimens from 80 IR-RMS patients on Children’s Oncology Group clinical trial protocols formed the training set. Tumors from 54 RMS patients across all clinical risk groups formed the validation set. Whole transcriptome profiling was performed using oligonucleotide microarrays employing nearly 1.4 million probe selection regions (PSRs) and used to derive weighted meta-features. Accuracies of protein-coding and non-coding meta-features to predict overall (OS) and event-free (EFS) survival were compared using areas under receiver operating characteristic curves. Associated biological processes were analyzed using curated pathway analysis tools. RESULTS: PAX-FKHR status was able to predict OS (p=0.041) and EFS (p=0.008) in the validation set, but not in the training set. Histologic subtype followed a similar predictive pattern. Cox regression on over 17,000 coding genes on the training set identified a prognostic 30-coding gene meta-feature (gMF; OS p=0.001, EFS p=0.012). A similar analysis on non-coding transcripts identified a 39-PSR meta-feature (ncMF; OS, EFS p&amp;lt;0.001). Both gMF and ncMF were able to predict OS and EFS (p≤0.023) in the validation cohort. Based on OS, predictive accuracy of ncMF was higher than gMF (96% vs. 71%, p&amp;lt;0.001). Analysis of biological processes using gMF showed enrichment for functions associated with musculoskeletal development and signaling pathways. Similar analysis of non-coding meta-features revealed enrichment for cellular assembly, cell cycle, apoptosis, and cancer-associated functions. CONCLUSIONS: A non-coding RNA meta-feature was able to better predict outcome in IR-RMS than a coding gene meta-feature, where most standard clinical prognosticators failed. The meta-features were independently validated in IR and non-IR RMS. This suggests that non-coding transcripts can regulate and determine RMS biology and aggressiveness, and be used as novel prognostic indicators. Citation Format: Anirban P. Mitra, Sheetal A. Mitra, Jonathan D. Buckley, Stephen X. Skapek, Douglas S. Hawkins, Timothy J. Triche. Coding and non-coding gene meta-features predict outcome in pediatric rhabdomyosarcomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5170.
Abstract 3793: Single-cell transcriptional lineage tracing reveals complexity and plasticity of breast cancer pro-metastatic phenotypes
Authors: Niccolò Roda, Andrea Cossa, Federica Ruscitto, Chiara Priami, Giada Blandano, Alberto Dalmasso, Francesco Bertolini, Pier Giuseppe Pelicci
Journal: Cancer Research (2022)

Abstract Breast cancer (BC) represents a major clinical hurdle, with metastasis being the most devastating events in patients’ history. Traditional lineage tracing approaches have been widely employed to study the metastatic progression of BC. However, these approaches were limited to the investigation of clonal dynamics and did not provide detailed molecular information underlying the pro-metastatic phenotype. In this work, we exploited a library of degenerated barcodes which were both integrated in the genome and expressed as synthetic transcripts. Coupling this barcoding strategy to scRNA-seq analysis, we tracked the clonal and transcriptional evolution of more than 37,000 MDA-MB-231 (a human triple-negative BC cell line) cells in vivo. Clonal distribution analysis revealed that BC clones display proliferative heterogeneity in primary tumors, with a small fraction of clones outcompeting all the others. Furthermore, primary breast tumors turned out to be composed of two phenotypically distinct groups of clones: highly proliferative, on one side, and migratory/stressed/stem-like, on the other. Then, we showed that the striking majority of metastatic cells derived from a single clone which was under-represented in its primary tumor. Importantly, these pro-metastatic clones upregulated genes and pathways associated with extracellular matrix (ECM) interaction, migration, and type-I interferon (IFN) response. In this scenario, independent pro-metastatic clones heterogeneously upregulated these pathways, thus suggesting a multi-faceted functional phenotype. Despite this variability, we detected a set of genes whose expression was statistically associated with the pro-metastatic phenotype, and we validated the relevance of these molecular features both at the biological and clinical level. First, we observed a reduced metastatic burden upon the in vivo knock-down of genes involved in ECM interaction (e.g., ANGPTL4, KCNQ1OT1, and ITGB4) and type-I IFN response (e.g., LY6E and IFI6). Second, we derived two compact gene signatures showing robust predictive power of BC patient survival, independently of other known clinical factors. In conclusion, our work revealed previously unappreciated cellular mechanisms of BC metastasization, providing new actionable therapeutic targets and new molecular predictors of BC prognosis. Citation Format: Niccolò Roda, Andrea Cossa, Federica Ruscitto, Chiara Priami, Giada Blandano, Alberto Dalmasso, Francesco Bertolini, Pier Giuseppe Pelicci. Single-cell transcriptional lineage tracing reveals complexity and plasticity of breast cancer pro-metastatic phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3793.
Abstract 5242: Rationally designing a radionuclide drug conjugate for heterogeneous genomically stable gastric cancer
Authors: Le Yang, Rui Xu, Peng Guo, Heqing Yi, Xiangdong Cheng
Journal: Cancer Research (2022)

Abstract Background: Gastric cancer (GC) is one of common malignant tumors with a 5-year survival rate less than 10% for patients in advanced stages. Genomically stable gastric cancer (GSGC), featuring the worst prognosis of four subtypes of GC based on its molecular characteristics, receives limited benefits from recently approved HER2-targeted antibody drug conjugates (ADCs) due to the lack and heterogeneity of tumoral HER2 expression. Currently, radionuclide drug conjugates (RDCs) have attracted great attention in targeted therapy, owing to its unique advantages of radiation-associated direct DNA damages and relatively long radiation distances. These unique advantages enable RDCs to efficiently and broadly ablate both cancer cells and cancer-associated stromal cells in heterogeneous tumor microenvironment without being internalized by target cells, featuring a novel and more efficient “bystander killing” effect than linker-cleavable ADCs. Here, a RDC was developed by rationally screening GSGC-targeting antibodies and radionuclides for targeted treatment of GSGCs. Meanwhile, the “bystander killing” effect of the RDC will be quantitatively determined and compared with conventional linker-cleavable ADCs. Results: First, ICAM1 was identified as a more preferable molecular target relatively to HER2 in human GSGCs via flow cytometric analyses. Next, a RDC was constructed by covalently conjugating anti-ICAM1 antibody with radionuclide 131I via indogen chemistry. 131I, the RDC warhead emitting β-particles with an emission range of approximately 0.8 mm, is a clinically-used radionuclide with FDA approval for treating several tumors. Then, the half maximum inhibitory concentrations (IC50s) of constructed 131I conjugated ICAM1 antibody (131I-ICAM1) was evaluated in human GSGC cell lines. Its inherent bystander killing effect was validated by measuring the viability of ICAM1 negative cells co-incubated with ICAM1 positive cells in the treatment of 131I-ICAM1, in comparison with ADC of the same target. Ultimately, the anti-tumor efficacy of 131I-ICAM1 was verified in heterogeneous GSGC models. Simultaneously, the histopathological damages to normal organs by 131I-ICAM1 was minimized by optimizing its dose. Conclusions: In this study, we constructed a rationally designed 131I-ICAM1 by conjugating ICAM-1 antibody and clinical available radionuclide 131I and investigated the intrinsic bystander killing effect of this RDC in ablating heterogeneous GSGCs. Our research explored the potential of 131I-ICAM1 as a promising targeted therapeutic candidate for treating heterogeneous GSGCs. Citation Format: Le Yang, Rui Xu, Peng Guo, Heqing Yi, Xiangdong Cheng. Rationally designing a radionuclide drug conjugate for heterogeneous genomically stable gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5242.
Abstract 3351: Identification of a NOTCH transcriptomic signature for basal-like breast cancer
Authors: Eike-Benjamin Braune, Felix Geist, Dirk Wienke, Anita Seshire, Urban Lendahl
Journal: Cancer Research (2022)

Abstract Breast cancer is a heterogeneous disease, where different subtypes differ in terms of biology, treatment options and prognosis. It is an emerging view that dysregulation of major signaling pathways, including Notch signaling, contributes to breast cancer development and progression, but precisely how and in which subtypes Notch signaling is hyperactivated still largely remains to be determined. To gain further insights into dysregulation of Notch in cancer, we set out to identify and evaluate a novel Notch transcriptomic signature focusing initially on breast cancer. We first established Notch-regulated transcriptomes from six breast cancer cell of the basal subtype in response to activation of Notch (by culturing on immobilized Notch ligand) or blockade of Notch (by γ-secretase inhibitors) for 8 and 72 hours. Our data reveal that a core set of Notch-responsive genes can be identified from both time-points and we are validating the transcriptomic signature on a broader set of additional 19 breast cancer cell lines from various breast cancer subtypes. The Notch transcriptomic signature showed robust coherence scores in breast cancer patient data, and robust correlation with basal-like transcriptomes was demonstrated. Analysis of single cell transcriptomic data from breast cancer patients demonstrated that the Notch signature showed additionally good correlation with immune cells in the tumor stroma, notably tumor associated macrophages, indicating that Notch signaling is upregulated in specific cell types of the tumor stroma. Collectively, the data provide a robust transcriptional signature to read out activation of the Notch signaling pathway in basal-like breast cancer and the signature will be important to better stratify patients and to evaluate and predict outcome of future Notch-based therapies for breast cancer. Citation Format: Eike-Benjamin Braune, Felix Geist, Dirk Wienke, Anita Seshire, Urban Lendahl. Identification of a NOTCH transcriptomic signature for basal-like breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3351.
Abstract 6051: Longitudinal single-cell sequencing of high-risk neuroblastoma tumors reveals intrinsic and extrinsic mechanisms of therapy resistance
Authors: Yasin Uzun, Liron D. Grossmann, Chia-Hui Chen, Anusha Thadi, Chi-Yun Wu, Peng Gao, Dinh Diep, Lea Surrey, Daniel Martinez, Tasleema Patel, Qi Qiu, Sarah Johnson, Wenbao Yu, Shane Drabings, Changya Chen, Yuxuan Hu, Gregory Chen, Derek A. Oldridge, Kun Zhang, Hao Wu, Kathrin Bernt, Nancy Zhang, John M. Maris, Kai Tan
Journal: Cancer Research (2022)

Abstract Neuroblastoma is a childhood cancer originating from embryonic neuronal progenitor cells and is the most common cancer diagnosed in infants. Although the majority of patients respond to initial chemotherapy, most high-risk patients suffer relapse due to therapy resistance. Here, we generated single-cell transcriptome and bulk whole-genome sequencing data of diagnosis and post-therapy samples from 23 patients diagnosed with high-risk neuroblastoma. We found two distinct adrenergic populations in the patient samples. Most tumor cells showed a mature sympathoblast phenotype whereas a small fraction was in an undifferentiated state with activation of protein translation, unfolded protein, and oxidative phosphorylation pathways. We found that therapy-resistant tumor cells in these two subpopulations had distinct characteristics. The more mature resistant subpopulation upregulated genes associated with epithelial-to-mesenchymal transition, including TWIST1, PLCB1 and CD276. The undifferentiated resistant subpopulation upregulated Ras signal transduction and TP53 pathway genes. Analysis of the immune microenvironment revealed that most tumor-associated macrophages became more immunosuppressive post-therapy via multiple newly gained signaling interactions including the complement signaling pathway. We discovered a limited infiltration of T lymphocytes in the tumor microenvironment, and chemotherapy induced an effector state with upregulated mTOR signaling and metabolism. Overall, our study revealed subpopulations of tumor cells in neuroblastoma that responded differently to induction chemotherapy. Our findings uncovered distinct molecular signatures of the resistant cells and their interactions with the immune microenvironment, paving the way for developing novel therapies for high-risk neuroblastoma. Citation Format: Yasin Uzun, Liron D. Grossmann, Chia-Hui Chen, Anusha Thadi, Chi-Yun Wu, Peng Gao, Dinh Diep, Lea Surrey, Daniel Martinez, Tasleema Patel, Qi Qiu, Sarah Johnson, Wenbao Yu, Shane Drabings, Changya Chen, Yuxuan Hu, Gregory Chen, Derek A. Oldridge, Kun Zhang, Hao Wu, Kathrin Bernt, Nancy Zhang, John M. Maris, Kai Tan. Longitudinal single-cell sequencing of high-risk neuroblastoma tumors reveals intrinsic and extrinsic mechanisms of therapy resistance [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6051.
Abstract 2743: Tumor growth modeling and its applications in preclinical pharmacology studies to improve translatability of animal models
Authors: Huajun Zhou, Binchen Mao, Sheng Guo
Journal: Cancer Research (2022)

Abstract Successful oncology drug development hinges on proper use of preclinical xenograft and allograft mouse models that faithfully reflect histopathology and genomic features of patient tumors and exhibit clinically similar drug response. For in vivo pharmacology studies, tumor-bearing mice are treated by drugs and efficacy is monitored by continuingly measuring tumor volumes over weeks or even months, resulting in mouse-specific tumor growth data. In our previous study, we used linear mixed model (LMM) to fit tumor growth data and demonstrated its superiority to simple readouts, such as tumor growth inhibition (TGI), in discovering biomarkers and exploring mechanism of action for drugs. Here we extended the study to more parametric statistical models on an expanded dataset with over 100 thousand tumor growth curves. Six parametric models (exponential, exponential square, Gompertz, Logistic, Monomolecular, and von Bertalanffy) were fit to tumor growth curves within a single-treatment group, by the least square method after variance stabilization transformation. Best models were selected by Akaike information criterion (AIC)-based confidence set. In the Irinotecan case study, logarithmic transformation of tumor volume converts exponential or exponential square models to mixed-effects linear models that integrate random effect of mouse difference within a treatment group, and are used to identify predictive biomarkers for drug efficacy. We compared three variance stabilization transformations and found that log transformation of tumor volume is appropriate for majority of tumor growth curves. In log transformed data, exponential or exponential square model can satisfactorily fit nearly all tumor growth curves, as 96.1% of best model confidence sets contain exponential or exponential square model, followed by von Bertalanffy (92.5%), Gompertz (92.1%), Logistic (90.5%), and Monomolecular (58.9%). The percentage of studies, in which all treatment groups can be best fit by exponential or exponential square model, is 91.3% (82.4% for von Bertalanffy, 81.4% for Gompertz, 79.1% for Logistic, 37.3% for Monomolecular). Thus, exponential or exponential square model can be applied to most in vivo mouse studies, and should be preferred for their mathematical simplicity and interpretability. We further demonstrated the utility of the modeling method on a 16-PDX-model study for Irinotecan to identify biological processes and pathways related to its mechanism of action. Citation Format: Huajun Zhou, Binchen Mao, Sheng Guo. Tumor growth modeling and its applications in preclinical pharmacology studies to improve translatability of animal models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2743.
Next-generation sequencing: unraveling genetic mechanisms that shape cancer immunotherapy efficacy
Authors: Ahmed Halima, Winston Vuong, Timothy A. Chan
Journal: Journal of Clinical Investigation (2022)

Immunity is governed by fundamental genetic processes. These processes shape the nature of immune cells and set the rules that dictate the myriad complex cellular interactions that power immune systems. Everything from the generation of T cell receptors and antibodies, control of epitope presentation, and recognition of pathogens by the immunoediting of cancer cells is, in large part, made possible by core genetic mechanisms and the cellular machinery that they encode. In the last decade, next-generation sequencing has been used to dissect the complexities of cancer immunity with potent effect. Sequencing of exomes and genomes has begun to reveal how the immune system recognizes "foreign" entities and distinguishes self from non-self, especially in the setting of cancer. High-throughput analyses of transcriptomes have revealed deep insights into how the tumor microenvironment affects immunotherapy efficacy. In this Review, we discuss how high-throughput sequencing has added to our understanding of how immune systems interact with cancer cells and how cancer immunotherapies work.